Quality management and competencies in forensic science.
Published In: Wiley Interdisciplinary Reviews: Forensic Science, 2024, v. 6, n. 3. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Neuteboom, Wim; Ross, Alastair; Bugeja, Lyndal; Willis, Sheila; Roux, Claude; Lothridge, Kevin 3 of 3
Abstract
The competencies and attributes of forensic science professionals are a very important pillar of a Quality Management system. However, a recent international survey identified a lack of agreement on the core cognitive competencies required for working in forensic science. The survey also identified the tools for assessing competencies are not necessarily designed to measure cognitive competencies. In this Perspective, we explore further the topic of competencies and in particular, cognitive competencies and attributes with a focus on forensic science professionals. We identify the critical issue and outline a process through which we will seek the views of leaders in the field, both operational and academic, on what the required cognitive competencies and attributes are for forensic science professionals. The tools used will include a questionnaire and direct interviews. Having identified the key competencies and attributes they can then be promoted, and methods developed to assess them within the recruitment process and continuous professional development programs. We argue that further discussion on this topic is warranted as it impacts on forensic science education, training, recruitment, operations, and overall quality. This article is categorized under:Forensic Chemistry and Trace Evidence > Presentation and Evaluation of Forensic Science Output [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Wiley Interdisciplinary Reviews: Forensic Science. 2024/05, Vol. 6, Issue 3, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2024
- ISSN:2573-9468
- DOI:10.1002/wfs2.1513
- Accession Number:177146312
- Copyright Statement:Copyright of Wiley Interdisciplinary Reviews: Forensic Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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